Biometric image-based identification systems have played a critical role in modern society in both criminal and civil applications. For example, criminal identification in public safety sectors is an integral part of any present day investigation. Similarly in civil applications such as credit card or personal identity fraud, print identification, for instance, has become an essential part of the security process. Among all of the biometrics (face, fingerprint, iris, etc.), iris and retina are the preferred biometric indicators for high security applications. However, verification systems based on fingerprints are very popular both for historical reasons and for their proven performance in the field, and facial image matching is the second largest biometric indicator used for identification.
An automatic biometric image-based identification operation, e.g., for enabling fingerprint, palm print, or facial image identification, typically consists of two stages. The first is the registration or enrollment stage, and the second is the identification, authentication or verification stage. In the enrollment stage, an enrollee's personal information and biometric image (e.g., fingerprint, palm print, facial image, etc.) is enrolled in the system. The biometric image may be captured using an appropriate sensor, and features of the biometric image such as, for instance, minutiae in the case of fingerprints, are generally extracted. The personal information and extracted features, and perhaps the image, are then typically used to form a file record that is saved into a database for use in subsequent identification of the enrollee.
In the identification/verification stage, a biometric image may be captured from an individual or a latent print may be obtained. Features are generally extracted from the image and, along with personal information, are formed into what is typically referred to as a search record. The search record is then compared with the enrolled (i.e., file) records in the database of the identification system. A list of matched scores is typically generated as a result of this matching process, and candidate records are sorted according to matched scores. A matched score is a measurement of the similarity of the features of the identified search and file records. Typically, the higher the score is, the more similar the file and search record is determined to be. Thus, a top candidate is the one that has the closest match.
With the advances in sensor technology in recent years, sensors used in capturing biometric images in both the enrollment and identification/verification stages have become much more compact. This decrease in size has also translated into a decrease in cost for manufacturing the sensors. For instance, some manufactures are now able to place a small non-optical fingerprint sensor, i.e. a solid state sensor, on a handheld wireless device such as a cellular telephone. In this instance, the capturing area of such a sensor is normally smaller than the size of the total area of the finger that needs to be captured, which may lead to difficulties in recognizing fingerprints acquired through these small-area sensors. An exemplary capture area for a solid state fingerprint sensor is only 300×300 pixels. Whereas, the area of the finger being captured may be on average three times as large.
The limitations with respect to fingerprint identification while using these small sensors result from the possibility that two impressions taken at different times from the same finger (e.g. during the enrollment stage and during the verification stage) may have a very small amount of fingerprint overlap area. Specifically, in the enrollment stage, typically only one file print image is enrolled (which is representative of only a portion of the actual finger print being captured), and features from this image are extracted and saved to be compared to a subsequent search print. If a minutiae-based matching algorithm is used, in the case of small overlap between the search and file prints, the number of mated minutiae will, likewise, be limited, which causes a loss in matching accuracy. The loss in accuracy may lead to an unauthorized person being misidentified as an authorized user, or an authorized person being prevented from using the application. In either case, the user is subject to significant inconvenience at best. Palm print identification using a sensor having an area smaller than the area of the palm that needs to be captured suffers from similar limitations as those described above with respect to fingerprint identification.
There are several known possible solutions to the above small sensor identification problem. However, each of these solutions has its own limitations. For instance, the size of the sensor may be increased, but this would typically lead to a more expensive sensor, thereby increasing the cost of the product that houses the sensor. Moreover, this may not be possible for some applications because of the small size of the product. Another solution is to use an image display to provide visual guidance while the user's images are being enrolled. However, it may not be practical in some applications to house such a display on the device due to size constraints, for instance, of the device. Still, another solution is to ask the user to put his finger in different positions while capturing his fingerprint during the verification stage. This solution is much more time consuming to the user during the verification process and, accordingly, may not be practical in real-world applications.
Yet another solution to the above small sensor identification problem is illustrated by reference to the flow diagram of FIG. 2. In this case, instead of a single print image being captured and stored as part of a file record for the enrollee, a mosaic fingerprint image is created and formed into a file record. To accomplish this, a fingerprint image is captured using a sensor (210) until it is determined (214) that the image is greater than a predetermined quality threshold. If the quality threshold is exceeded for the image, the image is enrolled as a file image (216). It is then determined (236) whether the number of enrolled images equals a desired, i.e., predetermined, number of enrolled images. If not, then steps 210 through 216 are repeated until the number of desired enrolled images is reached. A mosaic image is then created (240) from all of the enrolled images. The features of this mosaic image are extracted (220) and the mosaic image and corresponding matching features stored as the file record (224).
This method cannot be easily applied in real world applications due to several problems associated with the method. For instance, the mosaic image assembly process itself is a matching process, which requires linking the ridges to corresponding ridges and valleys to corresponding valleys, of the plurality of captured images, without any error. However, due to image distortion and noise and other uncertainties in image capture, this is typically not achievable. Correspondingly, the mosaic image created will generally not have smooth transitions in the boundaries between the separate captured images. Such limitations with respect to the generation of the mosaic image will lead to falsely detected minutiae during the verification stage, which leads to a lower matching accuracy.
As stated above, facial image matching is the second largest biometric used for identification. It has been implemented, for instance, in video-surveillance identification, entrance control, and retrieval of an identity from a database for criminal investigations. A benefit of this type of identification is that the acquisition process is non-intrusive and does not require collaboration of the person. However, a limitation is that, in general, the facial image expression or the captured angle of view may be different from the enrolled image or images, which causes a loss in matching accuracy. Capturing a plurality of different images from different angles of the face and with different facial expressions, during the enrollment stage, may solve the accuracy issue. However, there is a practical limit on the number of facial images that may be captured due to storage limitations of the system and due to a desire to keep the match time associated with the additional enrolled images to an acceptable level.
Thus, there exists a need for a method and apparatus for determining and storing an acceptable number of biometric images, such as fingerprints, facial images and palm print images, for use in biometric authentication when the identification system includes a sensor having an area that is smaller than the area of the biometric being captured. It is further desirable that the method increase the chances of a correct identification and decrease the chances of a misidentification during the verification process.